Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization

Choi, Zhi Chuan; Ang, Koon Meng; Lim, Wei Hong; Tiang, Sew Sun; Ang, Chun Kit; Solihin, Mahmud Iwan; Juhari, Mohd Rizon Mohamed; Chow, Cher En

doi:10.1007/978-3-030-70917-4_17

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…A chaotic OBL initialization scheme that combines Chaotic Mapping with OBL is adopted. The purpose of this method is to create a high‐quality initial population, enhancing population diversity and accelerating algorithm convergence (Choi et al, 2021; Kuang et al, 2014). Tent Mapping is a straightforward and uniform chaotic mapping function known for its strong ergodic properties.…”

Section: Trajectory Optimization Based On Multiobjective Golden Eagle...mentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

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Section: Trajectory Optimization Based On Multiobjective Golden Eagle...mentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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“…Years PSO (Particle swarm optimization) 2016 [77], 2018 [78], 2019 [79], 2020 [80,81], 2021 [82] CS (Cuckoo Search) 2016 [83], 2018 [84], 2019 [85,86], 2020 [87]…”

Section: Different Evolution Withmentioning

confidence: 99%

Parameters Identification of a Permanent Magnet DC Motor: A Review

Fazdi¹,

Hsueh²

2023

Electronics

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Since permanent magnet direct current (DC) motors are mainly used in various industrial automation applications, the demand for electric motors is increasing rapidly. However, in the mass production of electric motors, often, only random inspections are used to check the specifications and performance of electric motors. For manufacturing or engineering application staff to have a more thorough understanding of the characteristics of the motor, it is necessary to conduct a full or quick inspection during the production process to ensure the quality of the electric motor. Based on this, this literature review reveals several methods and algorithms often used to estimate DC motor parameters, given the importance of knowing the parameters of the DC motor and the lack of research on estimating the parameters of the DC motor.

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“…SI algorithms, including artificial bee colony (ABC) [1] and bat algorithm (BA) [2], are motivated by the cooperative behavior of animals, while the EAs, such as genetic algorithm (GA) [3] and differential evolution (DE) [4], are inspired by the Darwin's Theory of Evolution. MSAs are widely implemented to solve various type of optimization problems with different complexity level [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], due to its strengths of fast convergence speed and promising global search ability.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the poor balancing of the explorative and exploitative search behavior of the conventional PSO tends to suffer with premature convergence in dealing with complex optimization problems. Extensive studies were performed in recent years to strengthen the performance of PSO in solving single-objective optimization problems (SOPs) [6,8], multi-objectives optimization problems (MOPs) [7], constrained optimization problems (COPs) [5], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Although signficant numbers of PSO variants were introduced in the past decades such as those reported in [5,8,17,18,[21][22][23][24][25][26][27], the high tendency of these PSO variants to suffer with premature convergence issue remains as an on-going challenge. There are several factors that can contribute to these undesirable issue such as the initial population with poor quality and strong dependence of particles on histrical best positions (e.g., personal best position, global best position and etc.)…”

Section: Introductionmentioning

confidence: 99%

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Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

et al. 2021

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Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

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Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization

Cited by 10 publications

References 20 publications

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Parameters Identification of a Permanent Magnet DC Motor: A Review

Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

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